Systems and methods for diagnosing and/or monitoring disease

ABSTRACT

A method for evaluating a gastrointestinal tract may include characterizing one or more disease parameters using objective measures obtained from imaging data of a gastrointestinal tract. The one or more disease parameters reflect a measure of at least one of lesions, ulcers, bleeding, stenosis, and vasculature. The method may also include using the one or more characterized disease parameters to classify a disease state.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/371,511, filed on Apr. 1, 2019, which claims the benefit of priorityfrom U.S. Provisional Application No. 62/651,937, filed on Apr. 3, 2018,which are incorporated by reference herein in their entireties.

TECHNICAL FIELD

The present disclosure relates generally to devices, systems, and/ormethods for diagnosing and/or monitoring disease using, for example,mucosal severity imaging. More specifically, aspects of the presentdisclosure pertain to devices, systems, and/or methods for quantifiablyor otherwise objectively measuring disease indicators using imagingdevices.

BACKGROUND

Inflammatory Bowel Disease (“IBD”) is a chronic disorder characterizedby chronic inflammation in the gastrointestinal (“GI”) tract. Thedisease affects 5-6 million people worldwide, with approximately 1.6million sufferers in the United States. Patients and health careproviders may spend substantial amounts of money per year treating IBD,with annual direct costs estimated between $11-28 billion in the UnitedStates. Moreover, people with IBD may have longer and more expensivehospitalizations with higher readmission rates than people without IBD.

There are two main types of IBD: Ulcerative Colitis (“UC”) and Crohn'sDisease (“CD”). CD can affect any part of the digestive system and ischaracterized with transmural involvement. Symptoms of CD includeabdominal pain, fever, cramping, rectal bleeding, and frequent diarrhea.The peak age for CD disease onset is between 15-35 years. UC affects thecolon only, with mucosal involvement. UC carries mild to severesymptoms, which are similar to the symptoms of CD. With UC,complications may be less frequent than with CD. Colectomy may be usedto treat UC. The peak age of disease onset for UC is between 15-30 and50-70 years old. 55% of IBD patients have UC, and 45% of IBD patientshave CD.

IBD is characterized by bouts of disease (also known as flare-ups). Acorrelation has been shown between inflammatory markers and gut motilitydisorders. For example, it has been reported that CD subjects have loweramplitudes for small bowel contractions with an increase of peristalsisfrequency. Motor abnormalities have been shown to be more frequent inpatients with active CD. Patients with inactive CD have also shownmarked gastrointestinal motor disorders characterized by reducedincident of small bowel contractions and increased incidence of singleor clustered propagated contractions. Periods of flare-ups may beinterspersed with periods of remission.

Conventional treatments focus on addressing present symptoms. However,it may be more desirable to prevent disease progression in order toobtain deep remission. Despite studies to determine biomarkers of IBD,and despite discoveries regarding clinical variables, serologicalmarkers, fecal markers, and genetic tests, no single test is predictive,and no monitoring system exists. Conventional methods for diagnosing andmonitoring IBD lack a robust ability to identify remission and diseasestates and to monitor progression of disease. For example, currentindices for evaluating IBD may not be as objective as needed.

SUMMARY

Examples of the present disclosure relate to, among other things,devices, systems, and methods for diagnosing and/or monitoring diseaseusing, for example, mucosal severity imaging. Each of the examplesdisclosed herein may include one or more of the features described inconnection with any of the other disclosed examples.

In one example, a method for evaluating a gastrointestinal tract mayinclude characterizing one or more disease parameters using objectivemeasures obtained from imaging data of a gastrointestinal tract. The oneor more disease parameters reflect a measure of at least one of lesions,ulcers, bleeding, stenosis, and vasculature. The method may also includeusing the one or more characterized disease parameters to classify adisease state.

The objective measures include at least one quantitative measure. Theone or more disease parameters may reflect a measure of at least one ofan ulcer or a lesion. The objective measure may include at least one ofdepth or size. The disease may be an irritable bowel disease. The methodmay further include comparing the one or more disease parameters to oneor more threshold values. The method may further include aggregating atleast two of the one or more characterized disease parameters. Using theone or more characterized disease parameters may include using theaggregated at least two of the one or more characterized diseaseparameters. The method may further include aggregating at least two ofthe one or more characterized disease parameters. Using the one or morecharacterized disease parameters may include using the aggregated atleast two of the one or more characterized disease parameters. Themethod may further include comparing the aggregated at least two of theone or more characterized disease parameters to at least one thresholdvalue. The one or more disease parameters may be one or more firstdisease parameters. The disease state may be a first disease state. Themethod may further include storing at least one of the one or morecharacterized disease parameters or the disease state. The method mayfurther include using an objective measure obtained from additionalimaging data of the gastrointestinal tract in order to characterize oneor more second disease parameters. The method may further include usingthe one or more characterized second disease parameters to classify asecond disease state. The method may further include comparing at leastone of the one or more second disease parameters to at least one of theone or more first disease parameters. The method may further includecomparing the second disease state to the first disease state. Themethod may further include, based on the classified disease state,providing at least one of an alert or a treatment. The imaging data maybe data captured at more than one location in a gastrointestinal tract.The classified disease state may be at least one of an active diseasestate or a remission disease state. The one or more disease parametersmay include parameters to reflect measures of at least two of lesions,ulcers, bleeding, stenosis, and vasculature. The one or more diseaseparameters may include parameters to reflect measures of at least threeof lesions, ulcers, bleeding, stenosis, and vasculature.

In another example, a method for evaluating a gastrointestinal tract mayinclude receiving data from at least one imaging device located in alumen of a gastrointestinal tract. The method may also include, based onthe data received from the at least one imaging device, characterizing adisease parameter. The disease parameter may reflect a measure of atleast one of lesions, ulcers, bleeding, stenosis, and vasculature. Themethod may further include comparing the disease parameter to at leastone threshold value.

Characterizing a disease parameter may include using a quantitativemeasure. The disease parameter may reflect a measure of at least one ofan ulcer or a lesion. The objective measure may include at least one ofdepth or size. The disease parameter may be a first disease parameter.The method may further include, based on the data received from the atleast one imaging device, characterizing a second disease parameter. Thesecond disease parameter may reflect a measure of at least one oflesions, ulcers, bleeding, stenosis, and vasculature. The method mayfurther include aggregating the first and second disease parameters. Thedisease parameter may be a first disease parameter. The threshold valuemay account for an earlier characterization of the same diseaseparameter. The method may further include, based on the comparison,characterizing the disease parameter as worsening, lessening, orremaining stable. The method may further include, based on a result ofthe comparison, providing at least one of an alert or a treatment.

In another method, a system for evaluating gastrointestinal motility mayinclude an imaging device configured to capture image data in agastrointestinal lumen of a patient. The device may be furtherconfigured to measure an objective measure obtained from the data inorder to characterize one or more disease parameters and provide the oneor more characterized disease parameters to classify a disease state.

It may be understood that both the foregoing general description and thefollowing detailed description are exemplary and explanatory only andare not restrictive of the invention, as claimed. As used herein, theterms “comprises,” “comprising,” or any other variation thereof, areintended to cover a non-exclusive inclusion, such that a process,method, article, or apparatus that comprises a list of elements does notinclude only those elements, but may include other elements notexpressly listed or inherent to such process, method, article, orapparatus. The term “exemplary” is used in the sense of “example,”rather than “ideal.”

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate examples of the presentdisclosure and together with the description, serve to explain theprinciples of the disclosure.

FIG. 1 depicts an exemplary mucosal severity monitoring system.

FIGS. 2-6 depict exemplary methods for evaluating a disease.

DETAILED DESCRIPTION

The present disclosure is drawn to for devices, systems, and methods fordiagnosing and/or monitoring irritable bowel disease using, for example,mucosal severity imaging. In particular, in at least some aspects, thesystems and methods may be used to diagnose, monitor, and/or predict IBDconditions, including flare-ups, by quantifying parameters such as sizeand depth of lesions or ulcers, bleeding, stenosis, and vasculature. Thedevices, systems, and methods for diagnosis and/or monitoring usingmucosal severity imaging described herein may also be used to monitorother conditions including, for example, other gastrointestinalconditions such as irritable bowel syndrome, gastritis, gastroesophagealreflux disease (GERD), Barrett's esophagus, polyps, colorectal cancer,chronic asthma, chronic bronchitis, peptic ulcer, dysphagia,cholecystitis, diverticular disease, celiac disease, and emphysema.Although IBD monitoring is referenced herein, reference to IBD shouldnot be construed as limiting the possible applications of the disclosedsystems.

FIG. 1 depicts an exemplary mucosal severity monitoring system 100.Mucosal severity monitoring system 100 may include one or more imagingdevices 104, one or more software applications 108, memory 112, one ormore therapy delivery systems 116, one or more sensors 120, medicalrecords 124, environmental data/sensors 128, medical personnel 132, andone or more users 136.

Imaging device 104 may be an imager capable of taking images includingoptical, infrared, thermal, or other images. Imaging device 104 may becapable of taking still images, video images, or both still and videoimages. Imaging device 104 may be configured to transmit images to areceiving device, either through a wired or a wireless connection.Imaging device 104 may be, for example, a component of an endoscopesystem, a component of a tool deployed in a working port of anendoscope, a wireless endoscopic capsule, or one or more implantablemonitors or other devices. In the case of an implantable monitor, suchan implantable monitor may be permanently or temporarily implanted.

Imaging device 104 may be configured to capture images at one or morelocations of the GI tract, including the esophagus, stomach, duodenum,small intestine, and/or colon. For example, during an endoscopyprocedure, an imaging device 104 carried on an endoscope or a tooldeployed in an endoscope may be advanced through various portions of theGI tract, and images may be captured at numerous locations in a givenportion or across portions. Imaging device 104, a device carryingimaging device 104, or another component of mucosal severity monitoringsystem 100, such as software application 108, may be capable ofdetermining the location of the GI tract where images were recorded. Forexample, imaging device 104, a device carrying imaging device 104, oranother component of mucosal severity monitoring system 100, such assoftware application 108, may be able to determine whether images weretaken in the esophagus, the stomach, the ileum, the right colon, thetransverse colon, the left colon, the rectum, or the jejunum. An imagingdevice 104 carried by a wireless endoscopic capsule may capture imagesat various points as it traverses the GI tract. An imaging device 104which is part of an implantable monitor may be fixed in one location ormay be capable of capturing images at multiple locations. For example,an implantable monitor may include numerous imaging devices 104 atdifferent locations within the GI tract. Imaging device 104 may captureimages continually or periodically.

Imaging device 104 may be configured to capture data relevant to actualsize and depth of lesions, ulcers, and/or other abnormalities within theGI tract. Size of a lesion or ulcer may range from a scale of 100micrometers to a few centimeters. Depth information captured may berelevant to classifying depth as being superficial, submucosal, and/ormuscularis. Imaging device 104 may also be configured to capture dataregarding the prevalence of impact of lesions or ulcers on a specificregion of the GI tract (for example, the esophagus, stomach, ileum,right colon, transverse colon, left colon, rectum, and/or jejunum).

Imaging device 104 may further be configured to capture informationregarding inflammation. For example, imaging device 104 may be capableof capturing data regarding vasculature including patchy obliterationand/or complete obliteration. Imaging device 104 may be configured tocapture information regarding structure of blood vessels includingdilation or over-perfusion. Imaging device 104 may also be capable ofcapturing data relevant to blood flow including perfusion informationand real-time perfusion information. For example, imaging device 104 maybe configured to capture data relevant to blood's permeation into atissue. Imaging device 104 may also capture data relevant to tissuethickening, which may be the result of increased blood flow to a tissueand possible obliteration of blood vessels and/or inflammation.

Imaging device 104 may also be configured to measure stenosis in the GItract. In other words, imaging device 104 may be configured to assessthe amount of narrowing in various regions of the GI tract. Imagingdevice 104 may also be configured to assess, for example, tissueproperties such as stiffness. For example, stiffness may be monitoredduring expansion of a balloon or stent to prevent unwanted fissures ordamage. Imaging device 104 may further be configured to assess bleeding.For example, imaging device 104 may capture data relevant to spots ofcoagulated blood on a surface of mucosa which can implicate, forexample, scarring. Imaging device 104 may also be configured to capturedata regarding free liquid in a lumen of the GI tract. Such free liquidmay be associated with plasma in blood. Furthermore, imaging device 104may be configured to capture data relevant to hemorrhagic mucosa and/orobliteration of blood vessels.

An implantable device including one or more imaging devices 104 may bedelivered via a natural orifice transluminal endoscopic surgery (NOTES)procedure, potentially during a colonoscopy. For example, during acolonoscopy, an incision may be made and a sensor may be implantedoutside of the lumen on the omentum. Use of such a procedure may providebenefits including use of an endoscopy suite rather than an operatingroom. If imaging devices 104 require a battery, a battery may be changedduring a routine colonoscopy. Imaging devices 104 may also be deliveredvia laproscopic surgery or a different surgical or non-surgicalprocedure.

Imaging device 104 may be in communication either directly or indirectlywith a software application 108, which may be stored on a processor orother suitable hardware. Imaging device 104 may be connected withsoftware application 108 by a wired or wireless connection. In thealternative, imaging device 104 may be in communication with anothertype of processing unit. Software application 108 may run on aspecialized device, a general-use smart phone or other portable device,and/or a personal computer. Software application 108 may also be part ofan endoscope system, endoscope tool, wireless endoscopic capsule, orimplantable device which also includes imaging device 104. Softwareapplication 108 may be connected by a wired or wireless connection tomemory 112, therapy delivery system 116, one or more sensors 120,medical records 124, environmental data input/sensors 128, medicalpersonnel 132, user 136, and/or personal data 140. Software application108 may be able to run on multiple platforms using data for the samepatient. Multiple users (such as a patient, medical professional, orother caregiver) may be able to access software application 108concurrently or otherwise.

Software application 108 or any other kind of processing unit mayanalyze signals from imaging device 104 and other inputs (such as sensor120, medical records 124, environmental data input/sensors 128, medicalpersonnel 132, user 136, and/or personal data 140) and extractinformation from the data obtained by imaging device 104 and the otherinputs. Software application 108 or any other suitable component mayapply an algorithm to the signals or data from imaging device 104 andother inputs. Software application 108 may store information regardingalgorithms, imaging data, or other data in memory 112. The data frominputs such as imaging device 104 may be stored by software application108 in memory 112 locally on a specialized device or a general-usedevice such as a smart phone or computer. Memory 112 may be used forshort-term storage of information. For example, memory 112 may be RAMmemory. Memory 112 may additionally or alternatively be used forlonger-term storage of information. For example, memory 112 may be flashmemory or solid state memory. In the alternative, the data from imagingdevice 104 may be stored remotely in memory 112 by software application108, for example in a cloud-based computing system.

Software application 108 may interface with a source of medical records124. Software application 108 may further communicate with a source ofenvironmental data 128. Environmental data source 128 may includeenvironmental data sensors. Software application 108 may facilitate theentry of data by a user via user 136 or by medical personnel 132 via amedical personnel input. Data may be entered by a user 136 and medicalpersonnel 132 either locally or remotely. For example, data regardingsymptoms such as diarrhea or cramps may be entered via a user 136 and/ora medical personnel 132. User 136 and/or medical personnel 132 may enterinformation regarding when therapies such as medications wereadministered (e.g., started or finished). The user input and the medicalpersonnel input may constitute the same or separate components ofmucosal severity monitoring system 100. Software application 108 mayalso communicate with personal data source 140. Medical records source124, environmental data source 128, the input from medical personnel132, the input from user 136, and personal data source 140 may beseparate or may be integrated with one another in various combinations.

Software application 108 may be configured to analyze data captured byimaging device 104 related to, for example, inflammation, lesions and/orulcers, stenosis, and bleeding, as described above. Software application108 may, alone or in combination with other applications, process datafrom imaging device 104 to, for example, evaluate vasculature. Forexample, software application 108 may be configured to detect patchyand/or complete obliteration of blood vessels. Software application 108may also be configured to assess perfusion (e.g., real-time blood flow)intensity in one or more areas of the GI tract.

Software application 108 may further be configured to processinformation regarding lesions, ulcers, and/or other tissueabnormalities. For example, software application 108 or anothercomponent of mucosal severity monitoring system 100 may be configured toaccurately measure the size (width and/or length) of a lesion and/orulcer and may also be configured to measure the depth of a lesion and/orulcer. Software application 108 may be configured to classify a depth ofa lesion and/or ulcer as superficial, submucosal, and/or muscularis.Software application 108 may also be configured to accurately identifyand assess the impact of lesions and/or ulcers on one or more specificregions of the GI tract (e.g., the esophagus, stomach, ileum, rightcolon, transverse colon, left colon, rectum, and/or jejunum). Forexample, software application 108 may compare the relative prevalence oflesions and/or ulcers across different regions of the GI tract. Forexample, software application 108 may calculate the percentage ofaffected surface area of a GI tract and compare different regions of theGI tract. As a further example, software application 108 may quantifythe number of ulcers and/or lesions in a particular area of the GI tractand compare that number with other areas of the GI tract. Softwareapplication 108 may also consider relative severity of ulcers and/orlesions in an area of the GI tract by, for example, classifying one ormore ulcers and/or lesions into a particular pre-determinedclassification, by assigning a point scoring system to ulcers and/orlesions based on severity, or by any other suitable method. Softwareapplication 108 may also aggregate its consideration of ulcers and/orlesions across different portions of the GI tract in order to evaluatethe GI tract as a whole or the health of some combination of areas inthe GI tract.

Software application 108 may also be configured to assess stenosis inone or more areas of the GI tract, or in the GI tract as a whole. Forexample, application 108 may utilize data obtained by imaging device 104or any other input (e.g., sensors 120) in order to assess the amount ofnarrowing in various regions of the GI tract. Software application 108may further be configured to assess bleeding in the GI tract using datafrom, for example, imaging device 104. For example, software application108 may be configured to detect spots of coagulated blood on surfaces ofmucosa, to detect free liquid (e.g., plasma) in a lumen of the GI tract,and/or to detect hemorrhagic mucosa.

Software application 108, along with one or more imaging devices 104,may be configured to quantify severity of one or more symptoms orcharacteristics of a disease state. For example, software application108 may be configured to assign quantitative or otherwise objectivemeasure to one or more disease conditions such as ulcers/lesions,inflammation, stenosis, and/or bleeding. Software application 108 mayalso be configured to assign a quantitative or otherwise objectivemeasure to a severity of a disease as a whole. Such quantitative orotherwise objective measures may, for example, be compared to one ormore threshold values in order to assess the severity of a diseasestate. Such quantitative or otherwise objective measures may also beused to take preventative or remedial measures by, for example,administering treatment through therapy delivery system 116 as discussedbelow or by providing an alert (e.g., to medical personnel, a patient,or a caregiver).

Software application 108 may store the results or any component of itsanalyses, such as quantitative or otherwise objective measures, inmemory 112. Results or information stored in memory 112 may later beutilized for, for example, tracking disease progression over time. Suchresults may be used to, for example, predict flare-ups and takepreventative or remedial measures by, for example, administeringtreatment through therapy delivery system 116 as discussed below or byproviding an alert (e.g., to medical personnel, a patient, or acaregiver).

In performing the types of analyses described above, softwareapplication 108 may rely on data collected by various components ofmucosal severity monitoring system 100. For example, softwareapplication 108 may rely on the types of data (described above) that maybe collected by imaging device 104. Software application 108 may alsorely on inputs from other sources, including, for example, sensors 120,medical records 124, environmental data 128, medical personnel 132, user136, and/or personal data 140.

Mucosal severity monitoring system 100 may also include a therapydelivery system 116. Therapy delivery system 116 may be in communicationwith imaging device 104, either directly or via software application108. Therapy delivery system 116 may be a component of, for example, animplantable device or an external wearable device. Therapy deliverysystem 116 may be a component of any of the delivery systems includingimaging device 104 or may be a component of a separate device. Therapydelivery system 116 may be used to administer drugs. Therapy deliverysystem 116 may also be used to administer other therapies such asneuromodulation therapy to, for example, block or stimulate nerves orother tissue, including via vagus nerve stimulation, peripheral nervestimulation, sympathetic nerve modulation, gastric stimulation, or othertissue modulation therapies. Therapy delivery system 116 may form partof a closed loop system and may deliver therapy automatically based upondata from inputs such as imaging device 104 and/or sensor 120 withoutuser input. In the alternative, therapy delivery system 116 may beutilized manually by a user and/or medical personnel by way of, forexample, software application 108 or another manual input. A componentof mucosal severity monitoring system 100 may also recommend a treatmentcourse to a patient, medical personnel, a caregiver, and/or anotherparty.

Sensors 120 can measure a wide variety of parameters regarding activityof the esophagus, stomach, duodenum, small intestine, and/or colon.Depending on the parameter measured, different types of sensors 120 maybe used. For example, sensor 120 may be configured to measure pH via,for example, chemical pH sensors. Sensors 120 may transmit pHmeasurements to, for example, software application 108 on a receiver orother device. Software application 108 may utilize a pH measurement inorder to identify a specific location in the GI tract. For example, a pHof 6.5-7.5 may be indicative of the mouth, a pH of 4.0-6.5 may beindicative of the upper stomach, a pH of 1.5 to 4.0 may be indicative ofthe lower stomach, a pH of 7.0 to 8.5 may be indicative of the duodenum,and a pH of 4-7 may be indicative of the small or large intestine.

As a further example, gastric myolectrical activity may be measured via,for example, electrogastrography (“EGG”). Gastric motility and/ordysmotility may be measured, via, for example, accelerometers,gyroscopes, pressure sensors, impedance gastric motility (IGM) usingbioimpedance, strain gauges, optical sensors, acousticalsensors/microphones, manometry, and percussive gastogram. Gut pressureand/or sounds may be measured using, for example, accelerometers andacoustic sensors/microphones. Respiration rate may be measured using,for example, accelerometers, gyroscopes, and/or transthoracic impedance.Respiration rate may be measured so that software application 108 oranother component can filter out respiratory activity from the GIsignals software application 108 is analyzing. Certain of sensors 120may be used only at certain times in order to conserve battery. Forexample, it may be desirable to perform a higher-frequency sampling withan EGG-type sensor 120 during rest or sleep to avoid external noise andobtain a cleaner signal.

Sensors 120 may also measure other factors which may have a correlationwith flare ups and may indicate quality of life. For example,accelerometers, gyroscopes, GPS sensors, temperature sensors, bloodpressure sensors, and the like may be used to measure factors such asposture; activity level; and sleep/waking cycles, including the depth,duration, and number of awakenings during sleep periods. Stress levelsmay be measured via heart rate sensors, galvanic skin response,respiratory sinus arrhythmia (using, for examples, sensors describedabove for respiration), or other autonomic tone measures. Stress levelsmay also be entered via manual input, for example via input by medicalpersonnel 132 or user 136.

Gastric dysrhythmia may be measured with the types of measurementsdescribed above for gastric myolectrical activity and gastricmotility/dysmotility. Sensors 120 may also measure electro-mechanicaluncoupling, which is where electrical activity is present butcontractile activity is lacking. Sensors 120 may include acoustic,pressure, and/or other types of sensors to identify the presence of highelectrical activity but low muscle response indicative ofelectro-mechanical uncoupling. When electro-mechanical uncouplingoccurs, sensors 120, alone or in combination with the other componentsof mucosal severity monitoring system 100, may measure propagation ofslow waves in regions such as the stomach, intestine, and colon.Software application 108 or another component of mucosal severitymonitoring system 100 may classify any dysrhythmia as bradygastria(decreased activity), tachygastria (increased activity), or arrhythmia(irregular activity) for each region, such as the stomach, intestine,and/or colon.

Imaging device 104, sensors 120, medical records source 124,environmental data source 128, input from medical personnel 132, inputfrom user 136, and/or personal data source 140 may be used to recordinformation in software application 108 regarding pain or discomfortlevels; time of day/week/month/year; dietary intake; and environmentalfactors such as light, temperature, and altitude. Sensors 120, medicalrecords source 124, environmental data source 128, input from medicalpersonnel 132, input from user 136, and/or personal data source 140 mayalso be used to input demographic or other external data into softwareapplication 108. Such external data may include medical data such asprior relapse or flare-up information, medication (e.g., NSAIDs,antibiotics, hormone replacement therapy, oral contraceptives,cyclooxygenase-2, prednisone), surgeries (e.g. appendectomy orcolectomy), comorbidities, and mental health information. Relevantexternal data may also include test data such as: gut microbiota,genomics, serological antibody markers, serological inflammatory markers(C-reactive protein, erythrocyte sedimentation rate (ESR), Interleukin(IL)-1Beta, IL-2, IL-6, IL-8, IL-10, IL-16, IL-2 soluble receptor, tumornecrosis factor-alpha (TNF-alpha), TNF-alpha soluble receptor,IFN-gamma), white blood cell count, intestinal permeability, endoscopyresults (mucosal healing, confocal laser endomicroscopy, magnifyingcolonoscopy, etc.), histology results, and fecal markers (e.g., fecalcalprotectin, lactoferrin, S100A12, Indium 111-labeled leukocytes,alpha1-antitrypsin, alpha2-macroglobulin, myeloperoxidase, PMNelastase).Relevant external data may further include personal data such associoeconomic status, major life events, social media feeds, andinternet searches.

Based on the data and information from imaging device 104, sensors 120,medical records source 124, environmental data source 128, medicalpersonnel 132, user 136, and/or personal data source 140, softwareapplication 108 may perform numerous analyses and generate various plotsor other data. For example, software application 108 may analyze dataincluding the quantitative and or otherwise objective measures discussedabove for lesions and/or ulcers, inflammation, stenosis, and/orbleeding.

Software application 108 may generate a notification if analysis of datafrom imaging device 104 signals an upcoming disease flare-up or aflare-up in progress. Software application 108 may consider informationfrom medical records source 124, environmental data source 128, medicalpersonnel 132, user 136, and personal data source 140 when determiningwhether to deliver a notification. A predictive notification may bepotentially generated by software application 108 up to days in advanceof a flare-up. Notifications generated by software application 108 maybe provided to a patient, a caregiver, and/or medical personnel.Information gathered by the software application 108 may be used toclassify patients based on risk of flare-up in order to aid withpredictive abilities. Software application 108 may also communicate withtherapy delivery system 116 and may deliver therapy automatically basedupon analysis of data from sensors 120 without user input. In thealternative, therapy delivery system 116 may be utilized manually by auser and/or medical personnel, e.g., after receiving an alert.

As shown in FIGS. 2-6 , a system such as mucosal severity monitoringsystem 100 as depicted in FIG. 1 may apply a variety of algorithms todata collected from imaging device 104, sensors 120, medical recordssource 124, environmental data source 128, medical personnel 132, user136, and/or personal data source 140. In particular, softwareapplication 108 may apply the algorithms. The algorithms may be aided bymachine learning. For example, data gathered from any of the sourcesabove may be used to train an algorithm to predict exacerbations orflare-ups. Information input regarding mediation may be used to, forexample, predict or otherwise consider a patient's response tomedication and enable a health care provider, patient, caregiver orother party to tailor medication treatments. Data from different sourcesdescribed above may be combined in various permutations in order toenable predictive diagnostics and/or treatment recommendations. Whilemucosal severity monitoring system 100 is used as an exemplary system,it will be appreciated that the processes depicted in FIGS. 2-6 may beapplied to data from other systems. System 100 as described with regardto FIG. 1 and any or all of the methods described with regard to FIGS.2-6 may enable notifying a patient, health care provider, caretaker, orother party of worsening symptoms in order to provide a window of timein which to administer therapy and potentially prevent hospitalizations.For example, the examples described above and herein may enable aresponse to a rapid onset of symptoms. System 100 as described withregard to FIG. 1 and any or all of the methods described with regard toFIGS. 2-6 may also be able to detect exacerbations of a disease in orderto alert appropriate medical personnel of the onset of an exacerbationso that a patient may obtain appropriate medical care. A diseaseclassification and/or notification may be based on an early or a latestage of worsening symptoms and/or slow or rapid onset.

FIG. 2 shows an exemplary method 200 for evaluating various diseasestate parameters. For example, mucosal severity monitoring system 100may calculate one or more of a lesion and/or ulcer value 210, a bleedingvalue 220, a stenosis value 230, and/or a vasculature value 240. Each ofthe above values may be a quantitative or otherwise objective measureand may be calculated by, for example, software application 108 andstored in memory 112. The above values may be based on data collectedfrom imaging device 104. The above values may also be based on data fromany other component of mucosal severity monitoring system 100 asdescribed with regard to FIG. 1 .

One or more of lesion and/or ulcer value 210, bleeding value 220,stenosis value 230, and/or vasculature value 240 may be combinedtogether to result in a disease state value 250. In the alternative,disease state value 250 may be the same as lesion and/or ulcer value210, bleeding value 220, stenosis value 230, and/or vasculature value240. The disease state value 250 may be generated by any suitablecalculation. For example, lesion and/or ulcer value 210, bleeding value220, stenosis value 230, and/or vasculature value 240 may be added,multiplied, or otherwise combined together to provide disease statevalue 250. A hybrid calculation method may also be used. For example,one or more of lesion and/or ulcer value 210, bleeding value 220,stenosis value 230, and/or vasculature value 240 may be provided with aweighting value which may be applied prior to combining the values. Forexample, one value may be more heavily weighted than other values incalculating a disease state value. Such weighting values may beidentical across patients or may vary across the patient population orbe individualized. Such weighting values may be static or may changeover time, by either manual input, machine learning, or any suitablemethod. While the illustration of method 200 contemplates assigningvalues to ulcers and/or lesions, bleeding, stenosis, and vasculature,method 200 can account for fewer than all of these parameters. Forexample, method 200 may only consider one of the parameters above or mayconsider a subset of these parameters.

FIG. 3 shows an exemplary method 300 for classifying a disease state.Method 300 may be used in addition or in the alternative to method 200.In step 310, a component of mucosal severity monitoring system 100 suchas software application 108 may assign a value to ulcers, lesions,and/or any other tissue abnormality. Such a value may be a quantitativeor otherwise objective measure of ulcers and/or lesions and may measureany of the qualities of ulcers and/or lesions discussed above withregard to FIGS. 1 and 2 . In step 320, a component of mucosal severitymonitoring system 100 such as software application 108 may assign avalue to bleeding. Such a value may be a quantitative or otherwiseobjective measure of bleeding and may measure any of the qualities ofbleeding discussed above with regard to FIGS. 1 and 2 . In step 330, acomponent of mucosal severity monitoring system 100 such as softwareapplication 108 may assign a value to stenosis. Such a value may be aquantitative or otherwise objective measure of stenosis and may measureany of the qualities of stenosis discussed above with regard to FIGS. 1and 2 . In step 340, a component of mucosal severity monitoring system100 such as software application 108 may assign a value to vasculature.Such a value may be a quantitative or otherwise objective measure ofvasculature and may measure any of the qualities of vasculaturediscussed above with regard to FIGS. 1 and 2 .

In step 350, a component of mucosal severity monitoring system 100 suchas software application 108 may determine a disease state value. Adisease state value determined in step 350 may be the same as thedisease state value 250 described above with regard to FIG. 2 . Thedisease state value calculated in step 350 may account for all or anysubset of the ulcer and/or lesion value calculated in step 310, thebleeding value calculated in step 320, the stenosis value calculated instep 330, and/or the vasculature value described in step 340. Thedisease state value may be equal to one of the ulcer and/or lesion valuecalculated in step 310, the bleeding value calculated in step 320, thestenosis value calculated in step 330, and/or the vasculature valuedescribed in step 340, or to any combination of those values.

In step 360, a component of mucosal severity monitoring system 100 suchas software application 108 may compare a disease state value calculatedin step 350 to one or more threshold values. For example, each valuecalculated in steps 310, 320, 330, 340, and 350 may be compared to adifferent threshold value. In the alternative, the values calculated inthose steps may be aggregated in different manners before being comparedto one or more threshold values. The threshold values considered in step350 may be indicative of boundaries for certain disease states. Forexample, the thresholds may define varying degrees of disease severity.The thresholds may also be indicative of, for example, a flare-up orother active disease state or a remission.

In step 370, the disease state may be classified by, for example, adisease's severity, its status (e.g., active or in remission), or anyother relevant disease characteristic. The comparisons of values withthe same or different thresholds may produce different results. Forexample, one threshold comparison may be indicative of, for example, anactive disease state, while another threshold comparison may beindicative of, for example, remission. One threshold comparison may beweighted more heavily than another in such a circumstance. For example,one of ulcers and/or lesions, bleeding, stenosis, and/or vasculature maybe weighted more heavily than the other indicators. In addition or inthe alternative, if a certain number of threshold comparisons areindicative of, for example, an active disease state or flare-up, thedisease state may be classified as an active disease state or as aflare-up. For example, only one indicator (either a value or a thresholdcomparison) of active disease or flare-up may be necessary to classify adisease state as an active disease state or a flare-up. In thealternative, a certain number of indicators (either a value or athreshold comparison) of active disease or flare-up may be necessary toclassify a disease state as an active disease state or a flare-up. Themagnitude of any one value and/or threshold comparison may also beconsidered. For example, one or more indicators (e.g., ulcers and/orlesions, bleeding, stenosis, and/or vasculature) may be severe enough toclassify a disease state independently or with less weight from theother indicators.

The disease state classification in step 370 may also account forcomparisons with previous disease states, which may be stored in memory112. For example, a disease state may be classified as more or lessserious than a previously assessed disease state. Therefore, diseaseprogression may be tracked quantitatively or using other objectivemeasures.

While the steps illustrated in method 300 contemplate assigning valuesto ulcers and/or lesions, bleeding, stenosis, and vasculature, method300 can account for fewer than all of these parameters. For example,method 300 may only consider one of the parameters above or may considera subset of these parameters.

FIG. 4 shows an exemplary method 400 for classifying a disease state andadministering treatment and/or providing an alert. Any of the steps inmethod 400 may involve quantitative or otherwise objective measures ofone or more characteristics. In step 410, a component of mucosalseverity monitoring system 100 such as software application 108 maydetermine a disease state value. Step 410 may be the same as or similarto step 350 as described with regard to FIG. 3 and may incorporate thesame factors and considerations as step 350. In step 420, a component ofmucosal severity monitoring system 100 such as software application 108may classify a disease state. Step 420 may be the same as or similar tostep 370 as described with regard to FIG. 3 . Step 420 and/or step 410may also involve comparing one or more disease state values tothresholds, as described above with regard to step 360.

In step 430, a component of mucosal severity monitoring system 100 suchas software application 108 may determine whether a disease statewarrants treatment and/or an alert. Step 430 may include two separatesteps—one for considering whether treatment is warranted and one forconsidering whether an alert is warranted. In the alternative, both analert and treatment may be considered in the same step. For example, thevarious indicators and values calculated in steps 310, 320, 330, 340,350, 360, and/or 370 as described with regard to FIG. 3 may beconsidered in determining whether treatment and/or an alert iswarranted. Step 430 may involve comparing one or more indicators orvalues described above with one or more threshold values. For instance,values or indicators above or below a certain threshold value may becorrelated with determining whether treatment is needed. Values orindicators above or below another value may be correlated withdetermining whether treatment is needed. In the alternative, thethreshold values for treatment and alert may be the same thresholdvalue.

In step 440, if a disease state does not warrant treatment and/or analert, then no alert may be provided and no treatment may beadministered. However, information about such an event may be stored in,for example, memory 112 for later consideration or comparison withearlier or later measurements. In step 450, if a disease state warrantstreatment and/or an alert, in step 450 such an alert may be providedand/or such a treatment may be administered. Any alert or treatment asdescribed above may be provided or administered. A treatment may beadministered by, for example, therapy delivery system 116. An alert maybe made to, for example, a user, medical personnel, and/or a caretaker.Such an alert may be made through, for example, a user interface of amobile application, desktop application, and/or other application.

FIG. 5 depicts an exemplary process 500 for evaluating a disease state.In step 510, a component of mucosal severity monitoring system 100 suchas software application 108 may evaluate whether the severity of lesionsand/or ulcers alone is indicative of a disease state. For example, anabsence of lesions and/or ulcers may be indicative of remission withoutneeding to consider other factors. As a further example, the number,size, and/or depth of lesions and/or ulcers may be sufficiently large soas to indicate an active disease state or flare up. For example, step510 may consider a lesion and/or ulcer value 210 as described withregard to FIG. 2 and/or a value assigned in step 310 as described withregard to FIG. 3 . If the severity of lesions and/or ulcers issufficient to identify a disease state, then a disease state may beidentified in step 520. On the other hand, if the severity of lesionsand/or ulcers alone is not indicative of a disease state, then bleedingmay be considered in step 530.

In step 530, a component of mucosal severity monitoring system 100 suchas software application 108 may evaluate whether bleeding alone isindicative of a disease state. Any of the indicators of bleedingdiscussed above (e.g., coagulated blood, free liquid, hemorrhagicmucosa, and/or obliteration of blood vessels) may be considered in step530. For example, step 530 may consider a bleeding value 220 asdescribed with regard to FIG. 2 and/or a bleeding value assigned in step320 as described with regard to FIG. 3 . If measurements regardingbleeding alone are sufficient to identify a disease state, then in step520, a disease state may be identified. If measurements regardingbleeding are insufficient to identify a disease state, then stenosis maybe considered in step 540.

In step 540, a component of mucosal severity monitoring system 100 suchas software application 108 may evaluate whether stenosis alone isindicative of a disease state. Any of the indicators of stenosisdiscussed above (e.g., number and degree of narrowings) may beconsidered in step 540. For example, step 540 may consider a stenosisvalue 230 as described with regard to FIG. 2 and/or a stenosis valueassigned in step 330 as described with regard to FIG. 3 . Ifmeasurements regarding vasculature alone are sufficient to identify adisease state, then in vasculature are insufficient to identify adisease state, then vasculature may be considered in step 550.

In step 550, a component of mucosal severity monitoring system 100 suchas software application 108 may evaluate whether vasculature alone isindicative of a disease state. Any of the indicators of vasculaturediscussed above (e.g., patchy and/or complete obliteration, blood vesselstructure, real-time perfusion information, and/or tissue thickening)may be considered in step 550. For example, step 550 may consider avasculature value 240 as described with regard to FIG. 2 and/or avasculature value assigned in step 340 as described with regard to FIG.3 . If measurements regarding stenosis alone are sufficient to identifya disease state, then in step 520, a disease state may be identified. Ifmeasurements regarding stenosis are insufficient to identify a diseasestate, then two or more factors may be considered together in step 560.

In step 560, a component of mucosal severity monitoring system 100 suchas software application 108 may consider together all of the factorsdescribed above or any subset of the factors described above. Forexample, the steps of method 400 may be followed in step 560. As afurther example, steps 350, 360, and 370 as described with regard toFIG. 3 may be completed in step 560. As a further example, the factorsconsidered together may be the same as or similar to disease state value250 as described with regard to FIG. 2 .

While FIG. 5 shows an exemplary order of consideration for variousparameters, the various parameters may be considered in any order. Inthe alternative, the parameters above or other parameters may beconsidered concurrently. Furthermore, while the parameters above aredescribed as exemplary, other factors may also be considered in additionor in the alternative. Data from any of the above steps may be stored inmemory 112.

FIG. 6 shows an exemplary method 600 for classifying a disease state. Instep 610, a component of mucosal severity monitoring system 100 such assoftware application 108 may determine a disease state value. Step 610may be the same as or similar to any or all of the steps in exemplarymethod 500 as described with regard to FIG. 5 , may be the same as orsimilar to step 410 as described with regard to FIG. 4 , and/or may bethe same as or similar to any of the steps in exemplary method 300 asdescribed with regard to FIG. 3 . The value determined in step 610 maybe any of the values considered in method 200 described with regard toFIG. 2 . For example, the value determined in step 610 may be the sameas or similar to disease state value 250.

In step 620, a disease state value determined in step 610 may becompared to a previous disease state value. A previous disease statevalue considered in step 620 may have any or all of the characteristicsof a disease state value considered in step 610 but may have beendetermined at a previous time. For example, a previous disease stateconsidered in step 620 may have been stored in memory 112. Thecomparison in step 620 may consider more than one previous disease statevalue. In step 630, a disease state may be classified. For example, instep 630, a disease state may be classified with reference to a previousdisease state. For example, a previous disease state may be used toestablish an individualized baseline for a particular patient. Baselinevalues may be changed over time manually, by machine learning, or by anyother suitable method. A disease state may also be classified asworsening, improving, or remaining stable. Classifying a disease statein step 630 may also include classifying a disease state trend overtime. A classification may depend on the rate of change in a diseasestate. For example, a rapidly developing disease state may be classifiedin a different manner than a slowly-developing disease state. Step 630may be the same as or similar to step 420 as described with regard toFIG. 4 .

Any of the systems or methods described above may also be used toaggregate patient data. The systems or methods described above maycompare information gathered from a particular patient to data collectedfrom other patients and/or data manually input regarding patientclassifications. For example, any of the systems or methods describedabove may make use of a library of conditions. The systems and methodsdescribed above may be used to stratify a patient with regard to theirrisk of exacerbation of a disease state. Such stratification may bebased on previous data collected from a particular patient or may bebased on data for a particular patient population or a patientpopulation as a whole.

While principles of the present disclosure are described herein withreference to illustrative examples for particular applications, itshould be understood that the disclosure is not limited thereto. Thosehaving ordinary skill in the art and access to the teachings providedherein will recognize additional modifications, applications, andsubstitution of equivalents all fall within the scope of the examplesdescribed herein. Accordingly, the invention is not to be considered aslimited by the foregoing description.

We claim:
 1. A system for evaluating a gastrointestinal lumen,comprising: an imaging device configured to capture image data in agastrointestinal lumen of a patient; and a processor configured to:obtain a first objective measure from first image data of thegastrointestinal lumen; characterize one or more first diseaseparameters using the first objective measure; use the one or morecharacterized first disease parameters to determine a first diseasestate; store at least one of the one or more characterized first diseaseparameters or the first determined disease state; obtain a secondobjective measure from second image data of the gastrointestinal lumen;characterize one or more second disease parameters using the secondobjective measure, wherein the second image data is obtained at a latertime than the first image data; use the one or more characterized seconddisease parameters to determine a second disease state; compare (a) atleast one of the one or more second disease parameters to at least oneof the one or more first disease parameters or (b) the second diseasestate to the first disease state; and based on the comparison, predict adisease state trend over time using algorithms aided by machinelearning, wherein predicting the disease state trend over time includespredicting a flare-up of an irritable bowel disease.
 2. The system ofclaim 1, wherein the first and second objective measures each includesat least one quantitative measure.
 3. The system of claim 1, wherein theone or more first disease parameters reflect a measure of at least oneof an ulcer or a lesion, and wherein the first objective measureincludes at least one of depth or size.
 4. The system of claim 1,wherein the processor is further configured to compare the one or morefirst disease parameters to one or more threshold values.
 5. The systemof claim 1, wherein the processor is further configured to aggregate atleast two of the one or more characterized first disease parameters;wherein use of the one or more characterized first disease parametersincludes use of the aggregated at least two of the one or morecharacterized first disease parameters, and comparing the aggregated atleast two of the one or more characterized first disease parameters toat least one threshold value.
 6. The system of claim 1, wherein theprocessor is further configured to provide at least one of an alert or atreatment based on the predicted disease state trend.
 7. The system ofclaim 1, wherein the processor is further configured to use at least oneexternal data to predict the flare-up, wherein the at least one externaldata includes one or more of demographic data, medication, prior medicaltreatments, mental health information, genomic data, antibody markers,inflammatory markers, histology results, and fecal markers.
 8. Thesystem of claim 1, wherein the processor is further configured toclassify a subject having the gastrointestinal lumen, based on a risk ofthe flare-up.
 9. The system of claim 6, wherein based on the predicteddisease state trend, the alert is provided by the processor; and inresponse to the alert, the treatment is automatically delivered to asubject having the gastrointestinal lumen.
 10. The system of claim 6,wherein based on the predicted disease state trend, the alert isprovided days in advance of the flare-up.
 11. The system of claim 6,wherein based on the predicted disease state trend, the alert isprovided to a medical personnel.
 12. A system for evaluating agastrointestinal lumen comprising: an imaging device configured tocapture gastric activity data in the gastrointestinal lumen; and aprocessor configured to: receive the gastric activity data from theimaging device; measure a respiration activity; identify a location ofthe imaging device in the gastrointestinal lumen; filter out therespiration activity from the gastric activity data; characterize adisease parameter based on the filtered gastric activity data; comparethe disease parameter to at least one threshold value; and based on thecomparison, automatically provide at least one of a treatment to asubject or an alert.
 13. The system of claim 12, wherein the diseaseparameter reflects a quantitative measure of at least one of an ulcer ora lesion, and wherein the quantitative measure includes a depth of theat least one ulcer or lesion.
 14. The system of claim 12, wherein thedisease parameter is a first disease parameter, and the processor isfurther configured to characterize a second disease parameter based onthe filtered gastric activity data received from the imaging device,wherein the second disease parameter reflects a measure of at least oneof lesions, ulcers, bleeding, stenosis, and vasculature.
 15. The systemof claim 12, wherein the disease parameter is a first disease parameter,wherein the at least one threshold value accounts for an earliercharacterization of the first disease parameter, and based on thecomparison, the processor is further configured to characterize thefirst disease parameter as worsening, lessening, or remaining stable.16. The system of claim 12, wherein the processor is further configuredto, based on the comparison of the disease parameter to at least onethreshold value, predict a disease state trend over time usingalgorithms aided by machine learning, wherein predicting the diseasestate trend over time includes predicting a flare-up of an irritablebowel disease.
 17. The system of claim 13, wherein: the at least onethreshold value accounts for the earlier characterization of the firstdisease parameter.
 18. The system of claim 12, wherein the location ofthe at least one imaging device is determined by a pH measurement.
 19. Asystem for evaluating a gastrointestinal lumen comprising: a sensorconfigured to capture data in the gastrointestinal lumen; and aprocessor configured to: obtain a first objective measurement from thedata of the gastrointestinal lumen; characterize one or more firstdisease parameters using the first objective measure; use the one ormore characterized first disease parameters to determine a first diseasestate; obtain a second objective measurement from the data of thegastrointestinal lumen; characterize one or more second diseaseparameters using the second objective measure, wherein the secondobjective measure is obtained at a later time than the first objectivemeasure; use the one or more characterized second disease parameters todetermine a second disease state; compare (a) at least one of the one ormore second disease parameters to at least one of the one or more firstdisease parameters or (b) the second disease state to the first diseasestate; and based on the comparison, predict a flare-up of an irritablebowel disease using algorithms aided by machine learning.
 20. The systemof claim 19, wherein the processor is further configured to provide atreatment based on the predicted flare-up of the irritable boweldisease, and, based on the predicted flare-up of the irritable boweldisease, the treatment is automatically delivered to a subject havingthe gastrointestinal lumen.